DHQ: Digital Humanities Quarterly
2022
Volume 16 Number 3
Volume 16 Number 3
Algorithmic Close Reading: Using Semantic Triplets to Index and Analyze Agency in Holocaust Testimonies
Abstract
The following article presents a digital humanities exploration of indexing and analyzing expressions of agency in Holocaust testimonies. Using a set of text analysis methods to identify, classify, and visualize “semantic triplets,” we show how attention to agency complements and extends conventional approaches to indexing. Our examples come from two corpora of Holocaust oral histories: the first were conducted in Displaced Persons camps in 1946 by an interviewer named David Boder; the second were conducted by the USC Shoah Foundation Visual History Archive in the 1990s. We focus on two salient testimonies from each corpus in order to describe the methodology and what the analysis of agency can contribute to the writing of “microhistories” of the Holocaust. Building on semantic web analyses, the methods provide a groundwork for the development of a graph database to search testimonies by agency and thereby provide historical insights about what people report they did and what was done to them.
1 Introduction
Oral histories conducted with survivors are infinitely rich sources for understanding
the complex events of the Holocaust. As first-person narratives, oral history
interviews with Holocaust survivors provide vital, eyewitness testimony about the
rise of Nazi Germany, the violent acts of genocide, and reflections on its aftermath.
The “era of the witness,” as Annette Wieviorka calls it, coincides with the
emergence of institutional recording initiatives, such as those at Yad Vashem, Yale’s
Fortunoff Video Archive, the US Holocaust Memorial Museum (USHMM), and the USC Shoah
Foundation Visual History Archive [Wieviorka 2006]. Altogether, these
archives have recorded well over one-hundred thousand hours of testimony and
preserved hundreds of thousands of individual stories.
Because of the sheer size of these collections, indexing systems are critically
important for accessing their content. As such, all of these collections can be
searched effectively by names, dates, events, places, organizations, and various
subject headings. This is because “noun-based ontologies” form the taxonomic
structure of most indexing systems and finding aids. Attuned to the objective realm
of what was said, these ontologies help make the general content of an oral history
discoverable and accessible to researchers and the broader community of listeners.
When users enter keywords into a textbox, search and retrieval works because the
content is consistently and thoroughly described by a standardized, controlled
vocabulary [Presner 2016].
Not surprisingly, this is also the approach of most entity-recognition software
packages, such as OpenNLP [Apache Software Foundation 2017] and SpaCy [Explosion AI 2020], which are very successful at identifying things said,
such as proper names, organizations, geographic locations, historical events, dates,
units of time, quantities, and other numerical units, such as percentages and times
of day. But one of the fundamental limits is the focus on extracting isolated
elements (primarily nouns) rather than larger narrative units that describe what
people did and what was done to them. The purpose of this paper is to ask: In what
ways can oral histories be mined computationally to index how survivors narrate
behaviors, actions, reactions, assessments, and modes of self-orientation and
personal agency? This is vitally important in Holocaust testimonies since agency is
often described within networks of violence and coercion. This paper explores a
method for extracting, characterizing, and interlinking “semantic triplets” to
index and analyze vectors of action and agency, rather than just discrete topics,
themes, or isolated nouns. In so doing, we complement conventional indexing to gain a
richer, nuanced understanding of the events, agents, and actions described in a
testimony.
A semantic triplet is a grammatical unit consisting of Subjects,
Relations, and Objects. Subjects are units
consisting of nouns, primarily people, pronouns, and proper names, as well as direct
modifiers of the subject. Relations are verb units including one or more
verbs, related prepositions, and modifiers like adverbs. Objects, both
direct and indirect, have a broader range of parts of speech and include regular noun
units, adjectives, adverbs, and more. For example, “I removed the yellow
patch”[1] is a semantic triplet expressed as active speech (something the
subject did), while “I was shipped to Auschwitz” is a semantic triplet
expressed as passive speech (something done by an unspecified agent to the subject).
By linking semantic triplets with descriptive, keyword metadata, we hope to show how
new patterns of agency and previously unrecognized “microhistories”
[Zalc and Bruttman 2016] can be highlighted and discovered within oral
histories. Rather than considering our method as a reduction or simplification of the
text into discrete units of data, we argue that semantic triplets form a paratext
that highlights the richness of the testimonial narrative by adding to existing
metadata and methods of indexing.
2 Indexing Approaches in Two Corpora of Holocaust Testimonies
2.1 Corpus #1: David Boder’s 1946 Interviews with Displaced Survivors
The psychologist and linguist David Boder has a unique place in the history of
Holocaust testimony collection because he was the first person to record the
voices of displaced survivors in their own words after the end of World War
II.[2] In the
summer and early fall of 1946, he conducted over a hundred interviews with Jewish
survivors and other displaced people in nine different languages (mostly in German
and Yiddish, but also in English, Russian, Polish, French, Lithuanian, Latvian,
and Spanish), using a relatively new technology called a wire recorder.[3] He carried out his interviews in Displaced
Persons (DP) camps and other safe houses in France, Switzerland, Italy, and
Germany, mostly with Jewish survivors of concentration camps and slave labor
camps, as well as with a smaller group of non-Jewish refugees consisting of
Mennonites and other Christians who had fled Soviet territories. The interviews
range in length from about 15 minutes to over four hours, with most about one-hour
in duration. He asked over 16,000 questions to his subjects in an attempt to
understand the historical events of the Holocaust and learn about his
interviewee’s personal story of loss and survival.[4] Today, Boder’s interviews are available online, as audio files
in their original languages as well as in English translation, through the
Illinois Institute of Technology’s “Voices of the
Holocaust” project.[5]
After the interviews were recorded, Boder spent nearly ten years preparing a
3,194-page anthology of the survivor reports, which he called Topical Autobiographies of Displaced People.[6] It consisted of typewritten
translations, which he marked-up and indexed, of 70 of the testimonies. He
developed the first indices for characterizing the experiences of Holocaust
survivors, including a “subjects and situations” index with more than 300
entries, a “geographic and ethnic” index with over 400 entries, a “persons
and organizations” index with close to 300 entries, and a “trauma
inventory” enumerating 46 types of trauma and violence experienced by
survivors. His “subjects and situations” index represented the first
controlled vocabulary for indexing Holocaust testimonies and included shared
experiences like deportations, selections, mistreatment, and killings as well as
personal experiences such as food deprivation, thirst, and screaming.
Our analysis below will focus on two of these testimonies: The first is an
interview he conducted on September 26, 1946, with a 34-year-old, Polish-Jewish
woman named Anna Kovitzka (given the pseudonym “Anna Kaletska” by Boder).
Kovitzka lost most of her family, including her young baby, who she had given to a
Christian Polish woman in the hopes that the child would be able to survive. That
Polish woman was later denounced by a neighbor, and the child was killed. The
hour-long interview, spoken in Yiddish, covers about eight years of her life and
moves between her birthplace of Kielce, escaping the Grodno ghetto, being deported
to Auschwitz, and, finally, at the end of the war, being driven back into Germany,
and ending up after the war in Wiesbaden, where she is interviewed. The second
interview is with a German-Jewish survivor named Jürgen Bassfreund, who was also
deported to Auschwitz, where he worked in a forced labor factory. Conducted in
Munich on September 20, 1946, Bassfreund describes the rise of Hitler in Germany,
the deportation and death of his mother, and the horrific conditions he endured
while being transferred between multiple concentration camps. We chose this
particular interview because Bassfreund – who later changed his name to Jack Bass
– was re-interviewed by the USC Shoah Foundation in 1997, in his home in
Alabama.
2.2 Corpus #2: The USC Shoah Foundation Visual History Archive
Containing about 55,000 audiovisual testimonies, the Shoah Foundation’s Visual
History Archive (VHA) is the largest digital archive of Holocaust and genocide
testimonies in the world. The videos range in length from several minutes to more
than ten hours, although most are between one and three hours in duration.
Indexers trained by the Foundation tagged the videos with keywords by linking
one-minute segments of video to one or more search terms in its 60,000+ word,
hierarchical thesaurus. In addition, transcripts are currently being created for
many of the testimonies in their original languages as well as in English
translation.[7]
Using a tool called a “Video Indexing Application,” the
majority of the testimonies were automatically separated into discrete, one-minute
segments, and an indexer was prompted to assign a keyword from the thesaurus to
the segment (although not every segment had to have an indexing keyword). The VHA
derived its indexing protocols from the National Information Standards
Organization’s Z39.19 standard for the construction, format, and management of
monolingual controlled vocabularies. While useful for identifying topics, people,
places, and events for general searching, the index tends to obscure individual
actions and agency, not to mention aspects of performative delivery, subjective
experiences, and expressions of questioning, doubt, and possibility [Presner 2016]. Even though the aim of the index is objectivity, it is
important to underscore that a human listener decided what to index and what not
to index; a human listener decided what indexing term to use and what indexing
term not to use; and a human listener decided if a given narrative segment should
be described by a keyword or not. What is missing in the indexing system’s “philosophy of pursued objectivity”
[USC Shoah Foundation 2006] are the specific semantic expressions used
by survivors to describe actions, give evaluative assessments, provide
orientation, and tell about other types of agency and activities, including
subjective experiences and everyday behaviors, reactions, and acts of
resistance.
In addition to the Jürgen Bassfreund/Jack Bass re-interview, our analysis will
focus on the testimony of Erika Jacoby. Jacoby, a Hungarian Jew deported to
Auschwitz with her family in the Summer of 1944, survived the camp with her
mother. Her family was among the hundreds of thousands of Hungarian Jews deported
to Auschwitz at the height of the mass murder operations at Auschwitz. She was
separated from her grandparents and other members of her family at the ramp of
Auschwitz-Birkenau and never saw them again. She was able to stay together with
her mother, and both were selected for forced labor at Auschwitz. Her testimony
was recorded in her home in Los Angeles in 1994. Like the testimony of Bass, the
interview follows the structure of a chronological life-story, beginning with
childhood, experiences during the War and Holocaust, liberation, and eventual
immigration to the United States.
The four testimonies discussed in this article are intended to provide a
proof-of-concept for a method to identify and index expressions of agency. Our
hope is that the methods can be used to complement conventional, subject-based
indexing and open new possibilities for cross-corpora analysis.
3 Methodology
Figure 1.
Processing and Categorizing Triplets 3.1 Semantic Triplets Extraction
At the highest level, the aim of our research method is the transformation of a
Holocaust archives relational database (keywords linked to testimony segments)
into a Holocaust triplets graph database (networks of semantic triplets derived
from the testimonial transcripts). As shown in Figure 1, we use both interview
transcripts and their metadata, while our main contribution, as discussed in this
article, is the identification, extraction, and categorization of semantic
triplets from the transcripts. Our process can be summarized as follows: First, we
identify and extract triplets algorithmically from the cleaned transcripts; then,
we use text mining methods to assign categories and cluster the triplets; finally,
the results are manually reviewed (human check, correction, and curation). After
that, they can be stored, along with the original metadata, in a Tableau dashboard
or eventually a graph database for querying, indexing, and visualization. We will
introduce these core methods below.
Triplestore and RDF store are databases optimized for storing and retrieving
semantic triplets ([Rusher 2006]; [Peter 2001]). In
general, triplets take the form of subject-predicate-object expressions and can be
searched through network relations between entities. Digital humanities scholars
have used semantic web methods, such as linked data, to facilitate interoperable
research structures to analyze social or cultural datasets [Hyvönen 2020] as well as to model scholarly processes in the
humanities by exploring the productive tension between formally structured,
semantic data and humanities scholarship [Bradley and Pasin 2017].
Inspired by both the database design of Triplestore and RDF store and their
humanistic applications, we have developed our own process of extracting semantic
triplets from spoken testimonies. Our approach is best situated in the field of
information extraction for domain specific documents (in our case, Holocaust and
genocide testimonies), while the methods and ideas are replicable in other domains
such as oral histories more broadly, autobiographies, and personal narratives.
Traditionally, researchers proceed by creating open information extractors using
token related patterns ([Yates et al. 2007]; [Fader et al. 2011]) or dependency parsing related features ([Wu and Weld 2010]; [Mausam et al. 2012]). With the advancement of machine learning
technologies, we now see a trend of switching the research focus from extracting
informative and broad coverage semantic triplets to producing canonical triplets
which are linguistically coherent [Angeli et al. 2015]. This trend of
research tends to focus on improving the extractors’ accuracy, and, to date, there
are few applications that use semantic triplets to analyze spoken, personal
narratives, which resist formalized structures. Our semantic triplets extraction
method is an automated, rule-based process to aid close reading and focuses on
extracting expressions of agency and statements that speak to memories of
individual and collective actions.
This triplet extraction method leverages a combination of chunk parsing, also
known as partial parsing, and domain specific dictionaries. It focuses on the
lower-level chunks of nouns and/or adjectives (NAP) and verbs, which are groups of
connected but non-recursive tokens with different levels in syntactic structures
of sentences.[8] We convert the basic linguistic representation of the
chunks’ rules with regular expression from the NP and VP expressions of Abney
(1996)’s rules as:
N
A
P
→
D
?
A
*
N
*
N
|
D
?
A
*
A
and
V
P
→
V
-
t
n
s
|
A
u
x
V
-
i
n
g
where
D
means determiners,
A
means adjectives,
N
means nouns,
V
-
t
n
s
means the variation of a verb depending on its tense,
A
u
x
means auxiliary, and
V
-
i
n
g
means the present participle of a verb. Using the natural language
processing tools in SpaCy, we further extend these rules for chunks using the
built-in Noun Chunk function and a customized Matcher of verb chunks.[9] Our extension includes more parts of speech as
the possible components around core nouns, verbs, and adjectives, and we link the
chunks together to check if we fully process the sentences.[10]
and
where
For example, Figure 2 shows a parsing tree with the different parts of a triplet.
In the sentence “I was clinging to the window,” we
detect the active agency of window clinging with “I” as
the subject noun chunk and “the window” as the object
noun chunk. In this case, the verb chunk includes the verb, an auxiliary verb, and
a preposition. Using this extraction method, in our four example testimonies, we
are able to extract more than 3,000 triplets. Some sentences may contain multiple
triplets, such as this sentence: “I went with her down to the
door, and there I stood across the street, hidden in the gate, and I saw how my
child was lying on the snow.” In this example, four triplets were
extracted: “I went with her,”
“I stood across the street,”
“I hidden in the gate,” and “my
child was lying on the snow.”
Figure 2.
A Parsing Tree with Lexical Chunks that Form a TripletWhen parsing longer sentences, we always include, in a separate column for each
triplet, what we call “context terms.” We define the context terms to
be all the related lexical chunks other than those included in the extracted
triplet. In the first triplet above (“I went with
her”), the contextual chunks are the following words: the door, the street,
the gate, my child, the snow. The context is especially important when we want to
understand an action embedded in a complex sentence or create network diagrams
from the triplets.[11]
3.2 Characterization: Action, Orientation, and Evaluation (AEO) Categories with Subcategories
One of our methods for characterizing semantic triplets is derived from Labov and
Waletzky’s [Labov and Waletsky 1997] social-linguistic model of
narratives, where clauses are characterized by temporal organization (the order in
which the subject narrates events and actions in the story), evaluative
description (personal assessments made by the narrator), and contextual
orientation (usually information provided by the narrator that helps orient the
listener). Statements of evaluation provide judgment about a person or situation,
while statements of orientation provide contextual or relational information.
Various scholars have developed tools or solved related problems using the Labov
model: For example, Swanson et al. [Swanson et al. 2014] created a part
of speech (POS) based algorithm for clause type detection and labelling based on
this model; and Saldias and Roy [Saldias and Roy 2020] used this model
to train a classifier to analyze and compare spoken personal narratives. Although
these applications to characterizing narratives focus on the clause level, the
Labov model is also applicable to our research because triplets are derived from
clauses and need to retain semantic information.
Not unlike the use of text mining to study literary “styles”
[Jockers and Underwood 2015], we use our text mining methods to
classify triplets by similar types of speech in order to discover relationships
between different types of expressions of agency in oral histories. We divide
descriptions of agency into four sub-categories: active, passive, coercive, and
speculative speech. Active and passive speech are, respectively, actions done by
the subject of the triplet and actions done by others to the subject; coercive
speech may have an active structure but a passive meaning since it represents
something the subject was forced to do (sometimes without mentioning the person or
group responsible for the coercion); and, lastly, speculative speech, often
expressed through modal verbs, represents an uncertain or imaginary action, or a
statement of possibility, desire, ability, or futurity. While these categories are
not perfect or definitive, they help to identify the general kinds of speech
describing actions. We summarize and provide examples for the two-level AEO
characterization system in Table 1.
| Categories | Examples | |
| Agency | Active | I removed the yellow patch. |
| Passive | I was shipped to Auschwitz. | |
| Coercive | My father had to leave his home. | |
| Speculative | They could consummate the terrible deed. | |
| Evaluation | I am not ashamed. | |
| Orientation | I remembered a Christian woman. | |
Table 1.
Two-level AEO Characterization System with ExamplesThe Triplets AEO Algorithm implements the above process as a basic
characterization system. In SpaCy, the algorithm takes tokens consisting of the
Relation Verb Chunks,
R
, and Object Noun Chunks,
O
. For each token in both
R
and
O
, there is retrievable syntactic information including (but not limited
to) a part of speech (POS) tag, a dependency tag, and the lemma form of the word.
We then put together this lexicon- and rule-based mechanism as shown in Appendix
I, Algorithm 1.
3.3 Characterization: Perpetrators, Victims, and Others (PVO) Categories
The tripartite approach to characterizing victims, perpetrators, and bystanders
has been around for decades in Holocaust historiography [Hilberg 1992]. While not absolute or unproblematic, we strive to disambiguate subjects and
objects in triplets when they are identified as people or groups. To do so, we
first used the Entity Recognition pre-trained models in SpaCy to determine the
category of a NOUN (people, organizations, geographic locations, and so forth).
We, then, manually classified the detectable entities, where possible, into victim
and perpetrator groups. For example, the phrases “the German Army,”
“the SS,” or “the Gestapo” are detected as organizations and marked as
Perpetrator, whereas the interviewee (“I” and “we”) as well as familial
units (parents, grandparents, sister, brother, and so forth) are marked as Victim.
Pronouns, however, were not disambiguated. This classification method will always
be incomplete, as many subjects and objects do not unequivocally belong to the
victim or perpetrator group. The “gray zone” contains bystanders,
aid-providers, “implicated subjects,” and people who moved across categories
and do not easily fit the binary ([Rothberg 2019]; [Luft 2015]). These and other such subjects and objects are also
extracted but not categorized, as they demand human scrutiny of context and,
indeed, prompt further questions and future research about the range of agents in
an oral history. For the ones that are certain, we created lists of victim and
perpetrator lemmas, which provided the base forms of the entity words in the
triplets.
4 Results and Applications
Figure 3.
Distribution of Semantic Triplet Categories in Four TestimoniesIn Figure 3, we show statistical summaries of the triplets in each of the four
testimonies. The triplets were algorithmically generated from the transcripts,
assigned categories by speech type, and evaluated for accuracy. The size of each pie
chart is scaled to the number of triplets extracted for each testimony. Boder’s
interviews are shorter than the Shoah Foundation interviews (and, of course, end in
1946, rather than the 1990s). Due to the large number of triplets, we needed to
develop ways to categorize and cluster the results. This entailed categorizing the
triplets into six types of speech, assigning Victim and Perpetrator identities to
certain subjects and objects, and experimenting with using a Wordnet-based
characterization of the objects to cluster them by lemma and categories.[12] For our initial version
of the triplet extraction process, 6.5% of the triplets were manually corrected due
to incorrect or incomplete subjects, verb relations, or objects. In addition, about
14% were manually removed from the original output because the triplet did not make
sense grammatically or was misidentified as a triplet. The challenges and
shortcomings of the triplet extraction process are discussed in section 5.2. To
better analyze the triplets and their characterization, we created an interactive
Triplets Dashboard, shown in Figure 4, which brings together the corrected triplets
output, an object lemmas bubble plot, and multiple other filters.
As a finding aid, the dashboard helps us query and sort the triplets by interview,
subject, object, speech type, and more. In the dashboard, a search for semantic
triplets can be initiated on any term, category, or type of speech both within a
given testimony or across a group of testimonies. In the screenshot, we see the
triplets around Bassfreund’s description of his deportation from Berlin to his
selection for forced labor in Auschwitz. They evidence a wide-range of speech acts,
including active speech about other people’s agency (“they gave
us six slices” and “they placed a can”), passive
speech (“we were put on trains” and “we were driven out of the cars”), coercive speech (“we
had to sign affidavits” and “we have to leave
Germany”), speculative speech (“I shall never forget
those screams”), evaluation (“it was
uncomfortable” and “the screams were terrible”),
and orientation (“I have forgotten something” and “the people had a premonition”). Looking further at the
contextual information for the coerced action of being forced to sign affidavits, for
example, we see the following terms: “our disloyalty,”
“Germany,”
“account,” and “hostility.” The
original sentence can be seen on the far right of the triplet output, as translated
by Boder: “Before being sent away, we had to sign affidavits that
we have to leave Germany on account of our disloyalty and hostility to
Germany.”[13]
Figure 4.
Semantic Triplets Dashboard in Tableau (with simplified objects and
context terms). Selection from Boder’s interview with Jürgen BassfreundAnother way triplets can be used is to identify instances of different types of
speech within a testimony. For example, leading up to and following his description
of being deported to Auschwitz, we can visualize all the passive and coercive
statements made by Bassfreund in which “I” or “we” is the subject. As shown
in Figure 5, there are over 50 such instances of passive and coercive speech
described in this section of his interview with Boder (about ten type-written pages).
Bassfreund emphasizes, with precise language, what was done to him and the Jewish
community by the Nazis (we were surrounded, taken, moved, driven out, loaded on, led
to, rubbed with, shoved into, sent into, transferred to) as well as what was
specifically done to him (I was forcibly taken, assigned, sent, transferred).[14]
Figure 5.
Selection of Bassfreund’s Passive and Coercive Speech (Boder interview), with
“we” and “I” as subject, respectivelyWe can now show our approach to semantic triplets extends and deepens topical
indexing by pairing the triplets with the human-created indexing terms. As developed
by both Boder and the Shoah Foundation, we see a significant continuity in terms of
topics and themes (places, time periods, people, conditions, and events) used to
index the testimonies. For instance, on pages 292-293 of Bassfreund’s testimony in
Topical Autobiographies, Boder indexed the testimony
with the following terms: bathing, clothing, hair cutting and shaving, professional
criminals, confiscations, punishments, and tattoos. Similarly, in segment 25 of Bass’
testimony, we see the following indexing terms from the Shoah Foundation: Auschwitz I
(Poland: Concentration Camp), camp forced labor, camp latrines, camp living
conditions, camp selections, Poland 1941 (June 21) - 1945 (May 7), prisoner hair
cutting forced labor, transfer from Auschwitz III-Monowitz (Poland: Concentration
Camp), and transfer to Auschwitz I (Poland: Concentration Camp). While these topics
certainly give us guidance for finding the overarching places, dates, conditions in
captivity, movement, and forced labor, the indexing is not at the level of sentences
or clauses and, therefore, we do not really know what Bassfreund/Bass did or what was
done to him. As we see in Figure 6, we produce a corresponding network of just a few
of the triplets derived from these specific parts of his testimonies. The
interrelated set of triplets provide information about un-indexed subjects and agents
(both victims and perpetrators), un-indexed actions (written on, picked up by, worked
in, filled, sent), and un-indexed objects (the formation, their stomach, a truck, the
crematories, human hair, mattresses, packages, and a mountain). Altogether, they
point us toward ways of using the extracted semantic triplets to read, mark-up, and
index testimonies by including microhistorical accounts of actions by both victims
and perpetrators.
Figure 6.
Selected Examples of Triplets related to Indexing Categories in Two
Testimonies. (a) Bassfreund (interviewed by Boder) (b) Bass (interviewed by the
Shoah Foundation)In the next section, we discuss some of the applications of the triplets to
identifying acts of resistance, detecting unindexed stories, and analyzing larger
semantic networks.
4.1 Microhistorical Acts of Resistance
While large-scale acts of resistance (such as the Warsaw Ghetto uprising or the
Sonderkommando rebellion in Auschwitz) are rightly known, there has been
comparatively less appreciation of the range of everyday, seemingly ordinary acts
of resistance by individuals. One historian who is working at the vanguard of this
reappraisal of Jewish agency is Wolf Gruner, who has delved into numerous police
and municipal archives in Germany to uncover the wide-range of acts of defiance,
opposition, and protest by Jews living in Nazi Germany ([Gruner 2011]; [Gruner 2016b]). In his studies of “microhistories,” he has unearthed thousands of examples of Jews
mounting both formal and informal protest, ranging from the filing of petitions
and government complaints to outright acts of defiance, such as sitting on benches
marked “Aryan only” or refusing to use the forced middle name of
“Israel” or “Sara,” to physical and armed resistance against Nazi
officials [Gruner 2016a]. For our purposes here, we are interested
in using our computational method of identifying networks of semantic triplets to
find underappreciated or largely unknown acts of everyday resistance within
Holocaust testimonial narratives. Our argument is that triplets point to a
wide-range of small-scale actions that would otherwise go unremarked, since they
do not, generally speaking, rise to the level of what might be indexed as “acts
of resistance.” Oftentimes, these actions are not even considered to be
“indexable content,” and thus they are extremely difficult to locate.
4.1.1 Example 1: Anna Kovitzka
Interviewed by Boder in 1946, Anna Kovitzka’s story is particularly poignant,
as she describes the violent separation and loss of her family and, in
particular, her efforts to save her baby girl who was born in the Grodno
ghetto. With her husband, she managed to escape the ghetto in the hopes of
finding someone to care for her child. Shortly after giving up the child to a
Christian woman who promised to protect her, Kovitzka was deported to
Auschwitz. The child, unfortunately, did not survive as the Christian woman was
denounced for trying to protect a Jewish baby. Examining the indexing terms
used by Boder to mark-up her testimony, we see a wide-range of themes and
topics, including: geographic locations (Wiesbaden, Kielce, Grodno, Slonim,
Lvov, and Auschwitz), living conditions and objects (epidemics, starvation,
thirst, illness, sleeping accommodations, and clothing), people (children,
Gestapo, Prisoners-of-War, family, and Gypsies), and events (looting,
childbirth, burials, flight, killings, escapes, work, bathing, rebellion, and
appells or roll calls in camps). The last group of events certainly raises
questions about actions, but without further analysis, we cannot know who was
looting, escaping, working, rebelling, or even giving birth to a child.
Moreover, without delving into the testimony, we cannot uncover specific
vectors of agency (such as who did what, how did they do it, and to whom).
By examining the semantic triplets around the topical indexing term
escapes, we can illuminate the specific agency described by
Kovitzka in this part of her testimony (Table 2). The triplets indicate the
actions of Kovitzka and her husband to escape the ghetto in order to save their
child: “went over” the wires, “set up a chair,”
“raised” the wire, “went
out” on the street, “removed” the yellow
patch, and, finally, “went down” the street. In each
case, we see active speech describing a set of actions that occurred in the
context of escaping the ghetto, even after she reports that sixteen Jews were
killed at the gate in a first attempt to escape. While this section of the text
is indexed by Boder as escapes,
curfew,
fences, and yellow star, the actual acts of escaping
the ghetto through the wires, removing the yellow Star of David, and going down
the street represent acts of resistance and agency that can be successfully
detected and added to the index through computational text analysis attuned to
semantic triplets.
| Subjects | Relations | Objects | Context and Coreference |
| Sixteen Jews | fell at | the gate | first attempt |
| I | went over | the wires | second attempt, my man, me, the child, her |
| my man | handed | the child | I, the wires, the second attempt, me, I, her |
| My man | set up | a chair | me |
| He | raised | one wire | Coreference: He = my man |
| He | handed | the child | me, Coreference: He = my man |
| I | went out in | the street | |
| I | removed | the yellow patch | |
| I | went down | the street |
Table 2.
Anna Kovitzka Triplets (around escapes)Using the dashboard to search on the object child in Kovitzka’s
testimony reveals a number of triplets that show her attempts to save her child
and, ultimately, the child’s fate: “I must save my
child,”
“My man handed the child,”
“she has taken my child,”
“she had picked up the child,”
“I don’t have my child,”
“she kept the child,”
“she loved the child,”
“my child was lying on the snow,” and “they buried the child.” The tragedy of Kovitzka’s loss
of her child forms the traumatic core of her testimony and, yet, there is
barely any indication of this from the indexing terms. The creation of a
semantic network around the term child would help us locate and
appreciate these critical actions and thus begin to index the testimony attuned
to expressions of agency (Figure 7).
Figure 7.
Building a semantic network from triplets related to the term
child in Anna Kovitzka’s testimony4.1.2 Example 2: Erika Jacoby
After being deported to Auschwitz and separated from her grandparents on the
arrival ramp, Jacoby and her mother are selected for the slave labor camp.
Their heads are shaven; they are deloused for lice; and they are given meager
clothes. Most of Jacoby’s testimony, then, focuses on everyday life in
Auschwitz, including forced labor, sleeping conditions, food rations, violence,
and abuse. While the associated triplets bear witness to her struggle for
survival, our indexing of agency in Jacoby’s testimony also included the
following groups of triplets. As shown in Table 3, after one passive
construction, we see five successive, active constructions and one triplet
characterized as orientation (considered as information to the
interviewer).
| Subjects | Relations | Objects | AEO Category |
| We | were | taken to a bath | Agency (Passive Speech) |
| we | were going by | a German officer’s area | Agency (Active Speech) |
| I | saw | a swimming pool | Agency (Active Speech) |
| I | jumped out of | the line | Agency (Active Speech) |
| I | dove into | the swimming pool | Agency (Active Speech) |
| I | swam across | the pool | Agency (Active Speech) |
| They | didn’t shoot | me | Orientation |
Table 3.
Erika Jacoby TripletsThis utterly astonishing action is not marked up or identified in any way
through the Shoah Foundation’s topical indexing. In fact, there is no word
search that will find this part of her testimony. Jacoby relays the story in
just a dozen sentences, comprising about 50 seconds of her two-hour testimony.
And yet it is a stunning act of defiance, risk, and agency.
Here is the portion of the testimony with a little bit of context to set up the
scene and reflect on its meaning:
We stayed six weeks in Auschwitz. We had many, many interesting events that happened to us in Auschwitz. I just want to tell you one that was very important. And I don’t think I have time to describe Auschwitz. And I’m sure that it’s well known. But we were– one, one day, we were taken to a bath. We were taken to a bath about once a week or once every other week. And as we were marching, we were go– we were going by a German officer’s area. And I saw a swimming pool. And I jumped out of the line, and I dove into the swimming pool. And I swam across the pool, and I got back. And they didn’t shoot me, and I survived. But of course, my mother almost died. I mean, it was such an irr– irresponsible act from my part. But I was young, and I, I needed to be alive. And I did that. [Jacoby 1994, 51:40–52:30]
Indeed, there were two “pools” on the grounds of Auschwitz: one for
prisoners and one for SS officers, near blocks 7 and 8. They were built as
reservoirs to store water rather than be used for recreational swimming. Today,
a sign clarifies the remains of the structure: “Fire
brigade reservoir built in the form of a swimming pool, probably in early
1944.” When Jacoby arrived in Auschwitz in the summer of 1944, the
pool would have been complete. The Shoah Foundation manually tagged these
segments of Jacoby’s testimony with the following keywords: camp intake
procedures, camp prisoner marking, camp family
interactions, loved ones’ contacts, prisoner
external contact, and a single person tag, her mother, Malvina
Salamonovits. Judging by these tags, one might expect her discussion to focus
on procedural elements of the camp, perhaps coupled with how she and her mother
survived together. There is nothing in the indexing that would indicate jumping
out line, diving into the pool, and swimming across. While we can certainly
read or hear Jacoby talking about this experience if we happened to land on
this part of her testimony, it remains an unindexed action, which
otherwise submerges a significant act of resistance and agency. Through the
computational extraction of triplets, we can index, hone in on, and appreciate
this defiant act.
After Auschwitz is liberated by the Soviet army, Jacoby relays that she and her
mother left the camp in search of food and safety from sexual assaults by
Russian soldiers. Over the next three and a half minutes (1:18-1:21), she
describes a set of actions that helped her and her mother survive. She also
tells of acts of revenge and anger committed by her 16-year-old self. Two of
the three segments are tagged by the Shoah Foundation as food
acquisition and looting (the third is not tagged at
all). These topical terms convey little about the agency involved and the kinds
of actions that a listener may want to search for and find in her testimony. In
Table 4, we reproduce the full set of 23 semantic triplets that our methodology
identified over these three minutes. All forms of agency are included as well
as all subjects and objects. The “we” subject refers to Erika and her
mother.
While food acquisition and looting are indexing terms
that signal the overall set of content, these terms tell us nothing specific
about what Erika and her mother did in the days after liberation; they tell us
nothing about the vectors of agency and reasons for their actions; and they
tell us none of the details of the actions conveyed in her testimony. And
perhaps more pointedly, the indexing terms tend to obscure more than they
reveal: Far from abstract topics, food acquisition involved the
specific act of stealing and eating a pig (a non-kosher act of desperation for
survival), while looting was connected to Jacoby’s immediate
post-War actions, which reflect personal anger, acts of revenge, and the
longing for a normal life. The objects she takes from a home are symbols of
family life, domesticity, and normalcy: a white tablecloth, an apron, and a
little silver cup. Her act of looting is to take these everyday,
material objects from somebody’s home, the first possessions that she acquires
after liberation from Auschwitz.
| Subjects | Relations | Objects | Segments |
| we | got out of | the camp | 1:18 |
| we | went into | town | 1:18 |
| we | looked for | a house | 1:18 |
| we | found | a very beautiful home | 1:18 |
| we | occupied | it | 1:18 |
| we | couldn’t lock | the door | 1:19 |
| we | barricade | it | 1:19 |
| they | broke into | the stores | 1:19 |
| I | shoved | a lot | 1:19 |
| we | had | a canvas bag | 1:19 |
| we | passed by | a butcher store | 1:19 |
| I | took off | half a pig | 1:19 |
| I | carried | it | 1:19 |
| my mother | saw | it | 1:19 |
| she | cooked | the pig | 1:19 |
| we | ate | it | 1:20 |
| we | got | sick | 1:20 |
| I | was trying to find | a way | 1:20 |
| We | stayed in | this town | 1:20 |
| I | want to mention to | you | 1:20 |
| I | didn’t bring | it | 1:20 |
| I | have | a memento | 1:20 |
| I | broke into | a house | 1:20 |
| I | had | so much anger | 1:20 |
| I | expressed | that anger | 1:20 |
| I | went into | that house | 1:20 |
| I | broke | the piano | 1:20 |
| many people | went into | homes | 1:21 |
| I | did not want | anything | 1:21 |
| I | took from | the house | 1:21 |
| I | took | a white tablecloth | 1:21 |
| I | took | an apron | 1:21 |
| I | took | a little silver cup | 1:21 |
| it | had | the initials | 1:21 |
| I | kept | it | 1:21 |
| I | longed to establish | a normal life | 1:21 |
Table 4.
Erika Jacoby Triplets (1:18-1:21)5 Discussion, Limitations, and New Work
Although our process of extracting and characterizing triplets creates new paratexts
that enhance our interpretation and ability to index testimonies by descriptions of
agency, there are a number of limitations and shortcomings. The major issue is that
“semantic triplets” – strictly speaking as subjects, verb relations, and
objects – can oversimplify narrative expressivity when truncated, extracted, or
decontextualized. Below, we will identify and discuss a number of limitations as well
as discuss how our newer, “meticulous” triplet extraction process (version 2.0)
addresses some of those problems.
5.1 Considering Coreferences: Pronoun Disambiguation and Entity Linking
As is evident in many of the triplets, pronouns often exist in subjects and
objects, and they are sometimes ambiguous. For example, there are multiple
references to “he,”
“she,”
“they,” or “them” where the pronoun could refer to a perpetrator in one
line but a victim in the following. Thus, a coreferencing solution for the
triplets needs to disambiguate pronouns or, at the very least, link triplets
together such that the coreference can be clearly identified. We experimented with
using NeuralCoref, a coreference resolution python package which annotates and
resolves coreference clusters using a neural network [Hugging Face 2019]. However, this pre-trained language model is based
on out-of-domain text data, and it is only effective about half the time, which is
not precise enough for our needs.
Thus, we are working on creating our own model of coreferencing for use in
Holocaust and genocide testimonies. This close-domain model was initially trained
by annotating testimonies using BRAT, a web-based annotation tool that allows
customized labels for entities and coreferences [Stenetorp et al. 2012]. Inspired by the annotation process of entities and coreferences in an English
literature dataset, we will use a customized set of entity types, including named
people, groups, communities, as well as other regular named-entities like
organizations and geo-political entities [Bamman et al. 2019]. Our hope
is that the training model, derived from a diverse set of annotated Holocaust
testimonies, will help us disambiguate pronouns throughout a corpus.
5.2 Revisiting the Chunk Method: A More “Meticulous” Triplet Extraction Process
Our chunk-based triplets extraction method is certainly not flawless. One
challenge is that important expressions that are not triplets are simply not found
(for instance, “we were separated”). Another challenge
is how to capture fuller object phrases and distinguish direct and indirect
objects. In this version, we capture both, and usually the direct object is part
of the triplet and the indirect object is part of the contextual information; in
the more “meticulous” version, we capture fuller object phrases by extracting
from the “context” as much as possible. Although the objects tend to be
longer, they are more complete and integrate the contextual elements within the
object phrase itself. This newer version results in both more triplets being
extracted and higher accuracy.[15]
An ongoing challenge, however, concerns certain sentences with dependent,
relative, or conditional clauses, which are not always captured completely or may
be broken apart in the course of the chunk method. This problem can be seen with
“if, then” statements or ones that begin with a form of negation. For
example, this sentence spoken by Bassfreund, is turned into three triplets: “And it was already night and the SS opened the doors and said if
we throw out the dead bodies we shall get some food.”[Bassfreund 1946] Getting food from the SS is dependent upon the
victims being forced to throw out dead bodies from the train. Although the
triplets are captured individually, maintaining the specific contingency of the
actions and understanding the reasons for them requires human review and
interpretation.
While network analysis may give rise to new narrative orderings that allow us to
see relationships across a testimony or corpus, we also preserve the narrative
sequence of the triplets in our extraction process since the context for a triplet
certainly matters both within an individual sentence and between sentences. For
instance, when Bass says: “Well, you could tell. These guys
were very rough and very bad. They used to beat you up, and scream, and holler,
and push you. And hit you with a– a rubber– piece of rubber there. It looked
like a rubber hose.”[Bass 1997] Our triplet extraction
method maintains the contiguity between the description of the men (former
criminals who the Nazis put in place as supervisors in camps) so that the last
triplet (“it looked like a rubber hose”) continues to
make sense within this context. Sequential groupings are one way to maintain
narrative continuity.
Another basic challenge that we face is extracting triplets from fragmented,
vernacular speech, which the process often has trouble parsing. Survivors
sometimes switch from first-person narration to second or third-person; sometimes
the sentences contain incomplete thoughts, repetitions, or stuttering; sometimes
the transcribed sentence lacks regular punctuation; and sometimes survivors quote
the speech of others directly or indirectly. The triplet extraction process has no
way of telling if a triplet refers to the quoted or attributed speech of someone
else. For example, Anna Kovitzka says: “Once my man said:
‘I can’t make peace with them. Our child must be saved even if we two
shall die.’”
[Kovitzka 1946] While two triplets are extracted (“I can’t make peace with them” and “our
child must be saved”), the triplets are not attributed to her husband
because the phrase “once my man said” is not part of
either triplet.
To take another example: In his interview with Boder, Bassfreund says: “We were rubbed with kerosene”
[Bassfreund 1946] with respect to the delousing process in
Auschwitz. That triplet is easily found; however, in his later interview with the
Shoah Foundation, he says: “And then they did some – used some
kind of delousing agent, they poured all over us.” No triplets were
found in the first version of our process, as the sentence is spoken with breaks
that less clearly connect the subject (“they”) to the verbs and objects. To
address this, our “meticulous” version of triplet extraction is based on a
set of finer-defined, semantic rules to extract noun and verb chunks. To make
chunks include more contextual information and complete grammatical structures, we
add a layer in between the sentence and chunk levels, which we call the “segment
level,” for chunk-boundary decision making, as shown in Figure 8. Even
though the sentence is not grammatically correct, the new extraction method is
able to find the triplets in this sentence, while the previous approach was not.
And although some objects and object phrases become longer than expected, we can
now extract a more complete context for the objects of a triplet. We are also
experimenting with categorizing object phrases based on semantic features like
word lemmas, WordNet synsets, and dependency parsing, which will create more
filters for large scale triplets retrieval.
Figure 8.
Example of the Three-level Chunk-based Triplets Extraction Method5.3 Other Improvements and Potential Applications
Based on the indexing and analysis of agency in the four example testimonies
discussed here, we have applied our “meticulous” method to over a thousand
Holocaust testimonies. The output creates a new paratext of triplets for each
interview and provides a set of additional metadata that can be queried, indexed,
and overlaid on existing metadata. The triplets are intended to function as
finding aids to supplement the original text by providing a new set of metadata
attuned to articulations of agency. One of the promising directions is to query
“shared experiences” (such as reports of and responses to discrimination)
across a corpus using the semantic triplets. What, for example, did hundreds of
different witnesses say about their memories of discrimination during the 1930s
and early 40s and how did they describe their individual reactions? How many other
people remember removing the yellow star from their clothes and what did they say
they did next?
Going forward, we hope to create a better finding aid and scaled-up visualization
tool, perhaps through an indexing dashboard supported by a triplets database.
Researchers can then query and visualize through the same interface to find
semantic triplets and generate associated networks. This interactive dashboard
could combine the flexibility of curation in the Triplets Dashboard in Tableau
with a Neo4j Network Visualization Dashboard. At the same time, we are continuing
to explore how searching for “agency” within a narrative can complement
standard thematic and topical searches through network clustering and category
clustering of triplets.
6 Conclusion
Semantic triplets are a text immanent way to computationally identify and index
descriptions of agency within oral histories. While conventional indexing is
important for finding overarching themes and topics, it relies on the creation of a
secondary scaffolding for searching within the testimonies, almost always attuned to
named entities, topics, and general themes. Indexing semantic triplets, on the other
hand, is primarily an algorithmic approach to identify specific expressions of agency
and their lexical context from transcripts. As such, we are able to link and analyze
multiple vectors of action and attune our reading and listening to networks of
agents, relations, objects, and contexts. Our approach always relies on and is
derived from the interviewee’s spoken (and transcribed) language. In this regard, it
is a kind of “algorithmic close reading” built on a paratext derived from the
original transcript. Although “distant reading” remains a powerful approach for
statistically summarizing the content of a corpus, digital humanities scholars have
also warned against the seduction of “delegating observation” and “delegating
interpretation” to machines [Underwood 2019, 157–158]. In
many ways, semantic triplets use some of the algorithmic tools of distant reading
(large-scale text analysis attuned to patterns, classifications, and clusters) to
enable humans to undertake close readings and perform interpretative queries. It is
up to us to figure out the meaning or significance of any description of agency in
the context of an oral history or corpus.
In this sense, our approach is also quite different from current machine learning
methods for information extraction systems. Although seemingly unpopular these days
from a natural language processing perspective, a fully rule-based and easily
interpretable system works well for our purposes of triplets extraction and analysis.
We can describe the algorithms we use based on conditional statements and clear rules
for additional human curation and correction. Computational indexing of agency shifts
the scale of analysis to individual mentions of agency and interconnected actions,
helping us read and listen to testimonies in ways that deepen our understanding of
what people report they did and what was done to them.
7 Acknowledgements
The research presented here owes a significant debt of gratitude to the USC Shoah
Foundation, particularly Stephen Smith, Samuel Gustman, Martha Stroud, Claudia
Wiedeman, and Crispin Brooks. All of the analyses presented in this paper were
performed at UCLA under the direction of Todd Presner. The co-authors, Lizhou Fan and
Todd Presner, are grateful to UCLA team members Anna Bonazzi, Rachel Deblinger, Kyle
Rosen, Michelle Lee, and Wanxin Xie. We also thank Anne Knowles, David Shepard,
Anthony Caldwell, Wolf Gruner, and Zoe Borovsky for their generous support and
feedback over the years.
Appendix
Figure 9.
Triplets AEO Algorithm, as an Example of High InterpretabilityNotes
[1] Removing the “yellow patch” refers to removing the yellow
star that Jews were forced to wear on their outer garments to identify themselves
publicly.
[2] Both before and after the time that Boder completed his audio
recordings, various Historical Commissions throughout Germany, Poland, Hungary,
and Eastern Europe also collected personal stories, primarily through written
questionnaires and interviews [Jockusch 2012].
[3] The
best biography of Boder and the significance of his recordings is The Wonder of their Voices: The 1946 Holocaust Interviews of
David Boder
[Rosen 2010].
[4] Boder and his
interviewees do not use the term “Holocaust,” as this term did not come
into common usage until the 1950s to describe the Nazi genocide of the Jews. He
does, however, refer to and attempt to explicate “the
concentration camp phenomenon” (xiv) in the preface to his book of
interviews, I Did Not Interview the Dead (1949).
He says that the “displaced” and “uprooted” people were “dislocated by a world catastrophe” (xviii), perhaps
referencing the Hebrew word Shoah or the
Yiddish word Churban, both of which were used
by his interviewees to describe the destruction of the Jewish communities of
Europe.
[5] Copies of the original wire spools were sent
to the Library of Congress, which, decades later, procured the technical means
to undertake the media transfer of the wire-recorded audio to tape. Later, they
were digitized by the Illinois Institute of Technology (IIT) and made available
as WAV files through the Aviary platform. The interviews can be accessed on the
Voices of the Holocaust website: https://voices.library.iit.edu/david_boder. The English translations,
annotations, and indexing were done by Boder for the testimonies in his book,
Topical Autobiographies of Displaced People
[Boder 1950-56].
[6] Boder’s full
indices are found at the end of Topical Autobiographies of
Displaced People
[Boder 1950-56, 3105–3159].
[7] The Shoah Foundation generously provided our team with 983
English-language transcripts prepared by ProQuest and an additional set of 900
German language testimonies prepared by the Freie University in Berlin. While
this article describes only the analyses conducted with English-language
transcripts, the methods are being developed for German, too.
[8] For simplicity and consistency, we still use the name
Noun Chunk to represent the extended version, i.e. nouns and/or
adjectives (NAP).
[9] Our
work is built on the pretrained SpaCy model “en_core_web_lg,” which is an
English pipeline with components including part of speech tagger, dependency
parser, and lemmatizer.
[10] Our method is
inspired by the built-in SpaCy function of noun chunk extraction, as described
in its python source code syntax_iterators.py.
[11] As discussed in section 5, we have also developed a
fuller, chunk-based extraction method that enables us to capture longer object
phrases with higher accuracy.
[12] The
process of assigning “victim” and “perpetrator” identities is very
incomplete due to the need to disambiguate pronouns (“they,”
“he,”
“she”) as well as account for proper names.
[13] The German original also shows both passive and coerced
speech: “Bevor wir abtransportiert wurden, mussten wir
unterschreiben, dass wir wegen staatspolitischer und feindlicher Umtriebe
Deutschland verlassen müssen.”
[Bassfreund 1946]
[14]
Examples in the original German, which all follow the semantic pattern of passive
speech in the English translations, include: “Plötzlich waren
wir umringt von der Leibstandarte, und wurden dann mit Füßen getreten und in
Autos verladen”; “Und dann kamen wieder Autos und
wieder wurden wir von der Leibstandarte zu einem ganz entlegenen Bahnhof in
Berlin gebracht und wurden dort einwagoniert”; “wir
wurden aus diesen Wagons herausgetrieben”; “wir
wurden mit Petroleum eingerieben.”
[Bassfreund 1946]
[15] The details of the “meticulous” version,
including the rules and sample data, are available in the Github repository:
https://github.com/lizhouf/semantic_triplets
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Published by: The Alliance of Digital Humanities Organizations and The Association for Computers and the Humanities
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DHQ has been made possible in part by the National Endowment for the Humanities.
Copyright © 2005 -
Unless otherwise noted, the DHQ web site and all DHQ published content are published under a Creative Commons Attribution-NoDerivatives 4.0 International License. Individual articles may carry a more permissive license, as described in the footer for the individual article, and in the article’s metadata.
